Map shows NYC's energy consumption, building-by-building

Gmoke sez, "This statistical model uses 'zipcode-level energy consumption data to estimate the average annual energy use for every tax lot—at practically building level—through all five boroughs of the city.' Included are estimateans for space heating, space cooling, water heating, and base electric applications such as lighting. 'This map will enable NYC building owners to see whether their own building consumes more or less than what an average building with similar function and size would,' said Professor Modi. 'This is the first time anyone has provided an estimate like this for New York City and the first time anyone has offered information to the public in the form of an interactive map.'"

“This is a critical issue,” said Modi. “While discussions frequently focus on electricity use, homes in New York City, whether a townhouse or a large apartment building, use far more energy in form of heat rather than electricity. Nearly all of this heat is obtained from heating oil or natural gas. In addition, current electricity distribution infrastructure in many urban areas relies on large amounts of electricity brought in from outside the city, making it difficult to support increased future use without requiring significant investment of resources and funds. We are looking at ways we can address both these issues—reducing our heating bills and increasing local electricity generation capacity.”

their Galaxies are spread out between NYC, NJ, Conn and PA, just to start locally to NYC

this is why you never heard a single word about wallstreet being unable to function after 9/11.

ontop of that the exchanges operate with a built in latency in stock tick. this was developed to “level” the playing field so that no trading house had an advantage by placing their trading cluster right next door to the NYSE. this built in latency is what allows them to all extend their clusters into galaxies for load balancing and DR in NJ PA & Conn

That’s really cool, but wouldn’t per capita numbers – or some other inverse density measure – be more useful? Obviously a 40 story building is going to use more energy per land area than a 4 story building, but that doesn’t make it less efficient. (In fact, it would almost certainly be more efficient.) Or am I misreading this?

Agree. The usage of the building also needs to be taken into account. A measure per square metre of usable floor space might be better. Perhaps some normalisation for person-hours of building usage (to account people density and for 24 hour occupancy versus “business hours”) would also add some useful filtering. The raw data presented here really is just a proxy for building height.

Not sure why this would be useful, since it would make an 8000-square-foot McMansion or office space appear just as green as an 600-square-foot New York apartment, because they might both use the same number of watts per sq-ft.

More floor space = more energy, but this is a bad thing, and not something that should be removed from the equation by dividing by it.

The number of people living in the space is the key metric. If four adults can live in NY and use 200 KWH/month, then they are far greener than 2 adults living in a huge suburban house using 1500 KWH/month.

This was my exact question… the energy consumption seems to directly correlate to the height of the buildings (as I remember them from a decade ago) so it seems to me that either that’s not being taken into account or highrises are just that much more inefficient (which is not what I would expect.) Looking at the square foot numbers though, it’s just land size and not floor space. So, ultimately meaningless. Unless I’m misreading this.

I don’t know that saying that buildings in NYC “use far more energy in form of heat rather than electricity” is accurate; or if it is if it will be accurate for long. Here in Toronto we’ve used more energy in air conditioning each year than in heating since at least 2000 and NYC is further to the south than we are.

Hospitals are pretty big consumers of both electric and thermal energy. They’re on all day, every day. In that regard, utilities like them because, compared to some other types of load, they’re very predictable and the also have relatively high Load Factor ( their Peak electric demand isn’t very much above their average electric demand).

I’m waiting to get my hands on the paper and review the methodology, because – at least from the limited data set that I’ve worked with – I can see some flaws in the numbers.
I’ve been developing on-site energy projects in New York for a couple years now and have the actual consumption data for a few buildings. There are big differences between Modi’s estimates and the actual consumption numbers (for these specific buildings, his numbers are way too high). I’ll come back with a follow-up comment what I get the chance to look at the formulas used.

Note that this does NOT show energy consumption building by building. It shows energy consumption block by block and uses only raw land area, not the usable square footage of buildings. So as others have pointed out above, the energy usage tends to correlate with the height of the buildings.

The building where I live, for example, is part of a complex of four identical buildings (that I’m fairly confident use very similar amounts of energy) with two buildings on each of two blocks. Yet one block shows up much darker than the other, presumably because it’s smaller.

My guess is that if you recalculated this by built square footage instead of raw land area, many areas of the image would be reversed, since a typical modern 50 story office tower makes much more efficient use of light, heat, etc. per square foot than a 6 story 100-year-old apartment building.

Correct-This map does *not* show energy use building by building. It shows energy use by zipcode, as allocated across taxlots based on area. Which isn’t a very useful way to render the data.

Overall it looks to be an interesting project, but the work is not far enough along to be published yet. These guys need to spend some time at the Columbia U Spatial Design Lab and learn how to make an effective map. Just because some software will let you do something doesn’t mean that you should.

Many comments have pointed out the correlation of energy use with building height. They already have this information but they are not using it. It is in the same file they got the taxlot sizes from, which includes building square footage, number of floors, building class (around 200 types), building age and renovation year, and for large buildings–a further breakdown of square footage by type of use.

It’s tricky to transform the zipcode level data to taxlot resolution. You would need to use some real world data from individual buildings to develop the model. Maybe when the methodology is published their map will make more sense.